A wide variety of rechargeable batteries are used today (in this specification and the appended claims, the term battery includes individual electrochemical cells as well as batteries which combine a number of electrochemical cells). Rechargeable batteries power a wide range of devices including things as diverse as cellular telephones, forklift trucks, backup power supplies, electric carts, electronic test equipment and so on.
Rechargeable batteries have finite lives. The ability of a battery to hold a charge and to deliver its rated current degrades with use and with the passage of time. Eventually batteries must be replaced. Also, batteries of types which have certain chemistries require periodic servicing. For example, cycling certain nickel-based batteries without fully charging and discharging them can cause the formation of crystalline structures within the batteries. This phenomenon, known as “memory,” decreases the ability of a battery to hold charge. A condition of low capacity caused by a memory effect can sometimes be cured by servicing the battery. Such servicing may involve fully discharging the battery and then fully recharging it.
Many businesses use significant numbers of rechargeable batteries. In such cases it can be difficult to keep track of which batteries are in need of replacing or servicing. There is a need for methods and apparatus for determining the states-of-health of rechargeable batteries. Such apparatus should be able to provide an assessment of the state-of-health of a battery quickly.
The state-of-health of a battery must be distinguished from the state-of-charge (SoC) and capacity of the same battery. Capacity is the maximum charge that the battery is capable of holding. The capacity of a properly functioning new battery is typically equal to or greater than the specified capacity published by its manufacturer. SoC is a measure of how much charge the battery is currently holding as compared to the battery's capacity. For example a battery having a capacity of 5.0 Ampere hours (Ah) which contains a charge of 2.5 Ah would have a SoC of 50%. State-of-health, by contrast, gives a more thorough picture of the overall condition of a battery by assessing how closely a battery meets its design specifications. For example, the battery referred to above would be considered to have a poor state-of-health if it had a specified capacity of 15 Ah. Assessing state-of-health however involves more than simply comparing a battery's current capacity with its specified capacity. For example, the battery referred to above would be considered to have a poor state-of-health if it had a specified capacity of 5.0 Ah, but had an undesirably high internal resistance.
One approach to measuring state-of-health quickly is to measure the internal resistance of a battery. In general, excessive internal resistance indicates that a battery is in poor shape. Internal resistance measurements take only a few seconds to complete and provide a reasonably accurate indication of a battery's condition, especially if a reference reading from a good battery is available for comparison. Unfortunately, internal resistance measurements alone can provide only a rough indication of a battery's state-of-health. Various battery conditions affect the internal resistance. For example, the internal resistance of a battery typically decreases for some time after the battery has been fully charged. After a few hours the internal resistance settles to a lower value. Temperature also affects internal resistance values. Many batteries contain protection circuits that further distort measured internal resistance values.
The problem of measuring the state-of-health of batteries is compounded by the fact that there are a very large number of different types of batteries. Each is designed to behave in a different manner. Rechargeable batteries may have any of a variety of battery chemistry types. Some common rechargeable batteries include: nickel cadmium (NiCd), nickel-metal hydride (NiMH), sealed lead acid (SLA), lithium ion (Li-ion) and lithium polymer (Li-polymer). Descriptions of various types of batteries can be found in Handbook of Batteries, 2nd ed. 1995, David Linden; McGraw-Hill, Inc. ISBN 070379211. Further, for each chemistry type, a variety of battery models are available from various manufacturers. Any system for measuring the state-of-health of a battery must take into account the specific type and model of battery in question.
Previous approaches to determining the health of batteries include smart batteries, battery monitoring systems which relate battery health to the internal impedance of the battery, and battery monitoring systems which relate battery health non-linearly to one or more electrochemical parameters of the battery.
Smart batteries are devices containing at least one battery integrated with a system for measuring the state-of-health or state-of-charge of that battery. Because the state-of-health measurement system is designed for use with a single specific battery the system does not need to be adaptable to different types of batteries. A smart battery typically includes provisions for monitoring electrical parameters such as voltage, current into the battery and current out of the battery. Smart batteries typically include a simple processor that applies predictive algorithms to estimate the battery's current state-of-charge. An example of a smart battery is described in U.S. Pat. No. 6,072,299. This patent describes a smart battery with maintenance and testing functions which maintains information related to the battery's need for maintenance and monitors conditions that indicate when the battery has reached the end of its useful life and should be discarded.
Smart batteries have several limitations. One limitation is that a system for measuring the battery's state-of-health must be integrated with each battery. This reduces the cost effectiveness of smart batteries. Further, to keep smart batteries cost effective the capabilities of the monitoring circuitry are generally limited. Such simplified circuits do not always provide reliable results.
Various stand-alone battery monitoring systems have been proposed. Some such systems measure the value of at least one electrochemical parameter of a battery as that battery ages. The way that the value(s) of the electrochemical parameter (or parameters) change with time is then related to the state-of-health of the battery. For example, U.S. Pat. No. 5,977,750 discloses a system for determining the health of a battery by tracking the degradation of the full charge capacity of a battery over time. As a further example, international patent publication WO 00/19578 discloses a battery monitoring system which computes intersections between temporal extrapolations of curves of internal resistance versus temperature with a predefined internal resistance versus temperature limit curve, thereby determining an expected useful life and hence the current state-of-health.
A disadvantage of such systems is that they require that periodic measurements take place as the battery degrades with age. A history of measurements of the relevant electrochemical parameters of the battery must accumulate before the degradation in those electrochemical parameters can be determined. Such battery monitoring systems cannot determine the state-of-health of a particular battery without having a history for that particular battery.
Some battery monitoring systems measure an internal impedance of the battery and attempt to relate battery health to the internal impedance. Such systems are described, for example, in Huet, A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries, Journal of Power Sources 70 (1998) 59–69. To date, such approaches have either not been as reliable as is desirable or have been undesirably complex and difficult to automate.
Some battery monitoring systems attempt to obtain more reliable indications of state-of-health by measuring a number of parameters and using a non-linear partial least squares analysis to identify a best fit between the measured electrochemical parameters of a battery and some mathematically defined state-of-health. For example, such an approach is described in Byers et al., J. Electrochemical Soc. 126 (1979) p. 720. The non-linear partial least squares analysis approach is undesirably computationally intensive. Further, using too few parameters provides insufficient data for proper correlation. Using too many parameters introduces noise. Either of these factors can severely hamper the reliability of a system based on performing least squares analysis.
In some prior battery monitoring systems, taking a measurement of a battery's state-of-health takes undesirably long. This is especially the case for systems which require that the battery under test be fully charged before a state-of-health measurement can be made.
There remains a need for a system for measuring the state-of-health of batteries which ameliorates disadvantages of the prior art. There is a particular need for such systems which can be adapted quickly to accommodate new types of battery. There is also a particular need for such systems which can quickly and reasonably reliably determine a battery's state-of-health.